Scalable data gathering method for crowd surveillance systems

Today’s mass events – such as festivals, celebrations, sport or political events – have a great popularity, but they carry the danger of stampedes and that the physical integrity of participants in the swollen crowd may be compromised. There are several cases where the amount of people that are in the area increases so much, that there are injuries or even deadly victims of the mass gathering. For these reasons, it has become justified to create systems for observing the crowd at a mass event.

While planing such a system, it is an important question to find the solution for storing the data, so that it can be processed quickly and easily in an accessible structure. Thanks to modern technology, a mass surveillance system can now be established by extracting information from mobile telephones using crowd sensing, so there is no need for a complex data collection infrastructure, and the problem can be solved at the application level of mobile equipments. Using the location data of the phones, we can measure and represent the distribution and course of the mass, in one word mass dynamics. By storing measured data, we can analyze the mass of a given event and we can forecast the mass dynamics for different periods of the day/week, and discover the paths preferred by the participants. This information will be visible to the organizators, the security and the medical staff on a map interface, assisting them in preventing possible mass disasters.

In my dissertation, I am presenting the development of a data storage procedure for such a mass monitoring system. Based on existing models, I had to build a database model that is capable of storing the data that will appear in real time, while on the other hand, it can structurally store data sets for subsequent processing and prediction.